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©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
State of the Art IOT: 

The Future IOT Factory Stack
Smart Factory@Consumer Goods day 2018
Prof. Dr. Markus Focke
Leipzig, November 27, 2018
Porsche Consulting
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
FH AACHEN | Prof. Dr. Markus Focke
Operations Management
2
Fußnote
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
3
Überschrift
Fußnote
My eye opening moment | Amazons logistics warehouse
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Hype Cycle shows a lot of science fiction – but IOT
Platforms and enabling technologies are present
4
PaaS = platform as a service; UAVs = unmanned aerial vehicles
Source: Garter (2017), 3 Megatrends That Will Drive Digital Business, https://www.gartner.com/newsroom/id/3784363, accessed 17.11.17
IOT
Gartner Hype Cycle for Emerging Technologies (2017)
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
5
Überschrift
Fußnote
Don’t be embarrassed! | Most companies are not I4.0
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Connected, transparency, assistance and decentrali-
zation are major elements of a smart factory
6
connected integration
Machines, devices, sensors and employees are
connected and communicate with each other
via internet-of-things oder world-wide-web.
Main task is consolidating of functional
separated information
decentralised decisions
Employees or machines make autonomous
decisions and measures are performed at the
lowest hierarchy layer as possible.
This principle delegates responsibility back to
the operational employees
Systems assist employees with provision of
aggregated, visual and understandable
information.
The target is to analyze the data and find the
right measures as suggestions
technical assistance
transparent information
Sensor devices enrich information systems of
digital factories to create a virtual twin of the
real world in real time.
Generation of data and providing information to
create real time transparency
Principles of the Smart Factory
Source: M. Hermann, T. Pentek, B. Otto: Design Principles for Industrie 4.0 Scenarios
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Technological innovation pushes companies from
„musicians to DJs of production technology“
7
Trends for 2022
- Ubiquity: Omnipresent computer power
and ubiquitous internet access are moving
services via cloud into the internet
- Big Data: Competence and willingness to
measure all customer actions, provide new
analytical insights and business models
- Artificial Intelligence: Learning algorithms
change our ways to find correlations and
causations
- Augmented reality and virtual reality lead to
completely new forms of displaying infos
- Raspberry Pi: Really small computer devices
and standard operating systems
- Python & Co: Establishment of simple to
use standard programming languages
Moving the barrier for innovation
(from mastering technological
issues) to understanding processes
and the need for improvement
Short-term improvements for
individual problems possible 

(RIP one-size-fits-all Solutions)
Using sensors and analytical
power-tools with almost no invest 

(as-a-service models)
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Software and analytics will rule manufacturing in the
future; does your organization have the skills?
8
If you went to bed last night as an industrial
company, you’re going to wake up today as a
software and analytics company.
– Jeffrey Immelt, former CEO of General Electric
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Obstacles are high investments, low qualification of
own personnel and ambiguity of potential gains
9
Obstacles for Implementation of Smart Factory
* „Bitte geben Sie (..) an, ob Sie darin ein großes Hindernis für die Einführung von Industrie 4.0-Anwendungen sehen.“

Source: EY, Industrie 4.0 Status Quo und Perspektiven, S. 28
low qualification of
personell
investment 

too high
security issues
missing standards
business model 

not clear
missing 

IT Knowledge
ambiguous 

potential gain
Total 100-499 employees 500+ employees
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Implementation in small steps (instead of big projects)
creates agile solution elements of a smart factory
10
Approach of Stepwise Implementation
Source: Porsche Consulting
Introduction of smart
factory in small steps
(call it CIP, agile,
SCRUM… whatever)
• Focus on specific
problem and its solution
• Individual solutions
ensure fitting solutions
• As-a-Service Models with
no investment
• AAS-platforms communi-
cate via standard APIs
• Cooperation with
specialists (= start-ups)
• Implementing solutions that solve real problems – no management toys
• Evaluation of benefits directly possible
• Low-risk strategy enables to take chances
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
• Using hard- and software that is available today
• Realisation is realistic and fits with available
means and time
• Proven fit with manufacturing needs and business
potential is visible
Right solutions:
target corridor
Job #1: Chose the right space of your solution
11
Solution Space
pen & paper
0% 100%
height of 

innovation
starship
enterprise
smart factory
show-case
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Culture, strategy and technology have to be in line for
a successful application of smart factory ideas
12
Success
Strategy
Technology
Culture
no realization

possible
missing 

acceptance
direction 

not clear
• Open for changes
• Collaboration
• Executive Buy-In
• Strong digital 

skills
• Cross-funktional
teams, no silos
• Risk taking attitude
• Continuous
improvement
• Clearly defined vision
• Digital strategy available
• Existence of decision-
making framework
• Possibility of big steps vs.
small improvements
• Digital vision
• Cloud
• API-based
• Automation
• Continuous integration and
implementation
• Service oriented architecture
• Analytics
• Social
• Mobile
Culture + Strategy + Technology = Success
Source: McKinsey Global Institute
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Consumer devices offer high frequent innovations at
low costs (much better than industry specific devices)
13
Consumer Devices
Source: Apple, Statista
10 Iterations in 10 years
• Average life cycle of consumer devices: 12 months
• Extreme economies of scales (with world wide sales) lead
to competitive prices (phones, tablets, 5G Devices)
• Most specialized solutions lead 

to x-times higher prices
Adaptation of your 

requirements necessary!
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Afraid of statistics? Don’t be!
14
Easy to use analytical software
Fußnote
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
It’s not you! Beyond others, start-ups are driving
innovation in the upcoming era
„Rather than coming from multinational firms as
in the past, innovation now stems largely from
research laboratories, digital platforms and
startups.
These are the players creating algorithms and
developing use cases, they are the brains behind
innovations in image recognition, natural
language processing and automated driving.“
15
Innovations come from Start-Ups
It’s not longer possible to buy innovation from multinationals.

You need to work with start-ups and think-tanks to achieve competitive advantage!
Source: roland_berger_ai_strategy_for_european_startups.pdf (2018)
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Work together with start-ups if you want to achieve
competitive advantage – but you need to change!
16
* MVP = Minimum Viable Product
Proof of technical
feasibility
Start-Up Working Scheme funding
• Development of a working
product (MVP*)
• Hypotheses about
customer requirements
• Hypothesen über
Kundenanforderungen
• Find customer #1
funding
Working with start-ups requires a positive drive to solve the problem - the classical customer-
supplier-relationship will not work; it also needs knowledge and engagement also at „customer“ side
• high pressure to get
product working
• happy to get customer
feedback/ requirements
• low on cash, but not profit
oriented
Validation of 

business case
• Re-Engineering MVP
(complete overdo)
• Learn real customer
requirements
• Pivoting
• Grow customer base
(references)
• high pressure to get
customers / references
• happy to get customer
feedback/ requirements
• need to show real
turnover
Organizational 

growth
• Mature product
• Stable organization
(service level, processes)
• Product with „fixed“
functionality
• high pressure to get sales
• not possible to fulfill every
customers wish
• standards oriented
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
PointNine, a venture capital investor from Berlin, is
observing the i4.0 start-up landscape; 250 entries
17
i4.0 Start-Up Landscape
Source: https://medium.com/point-nine-news/industry-4-0-reinventing-the-factory-stack-bc9054398efa
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
The (currently innovative) future factory stack consists
of 11 elements
18
11 Elements of the Future Factory Stack
Source: Robin Dechant, PointNine Capital, Berlin
Prototyping/
Finding Supply
Rapid prototyping with the help of 3D
printing, platforms to find suitable suppliers
and vertically integrated factories.
Engineering
Tools
3D modeling, prototyping tools and
simulation platforms for designing products.
IoT /
Middleware
Get data from machines, connect offline
devices with online services.
Shopfloor
Guidance/
Apps
Enhance work instructions for complex
processes, process security and to ensure
the quality of production.
Wearables
Touch interfaces are ubiquitous in B2C and
people are used to personal devices. This
trend is recognizable in the industrial world.
Robotics
Software to program robotic behavior or
autonomous (co-) robots.
Energy
Monitoring/
Analytics
Monitor, analyze and optimize energy
consumption.
Analytics/
Efficiency
For a 360° overview and full control of the
whole production process. Measure and
analyze human workers and machine work.
Inspection
Companies that help in discovering issues on
the assembly line, e.g. with the help of
computer vision.
Predictive
Maintenance
Solutions for condition monitoring,
optimizing performance and reducing
downtime.
Asset Tracking/
Location
Analytics
Get transparency across the supply chain
with the help of tracking devices and
predictive / prescriptive analysis.
State of the Art IOT:

The Future IOT Factory Stack
Smart Factory@Consumer Goods day 2018
Prof. Dr. Markus Focke
Leipzig, November 25, 2018
Source: EY, Industrie 4.0, Status Quo und Perspektiven (2017), S. 40; n = 557 industrial companies
Flexibility, response time and equipment productivity
are i4.0 goals - cost reduction not so much
Motivation for Industry 4.0
20
Increase of
production flexibility
Faster response
times
Increase of OEE
Optimization of
customer support
Development of
innovative products
Cost reduction
0 % 20 % 40 % 60 % 80 %
23 %
25 %
33 %
47 %
52 %
72 %
Question: „Please rate if Industry 4.0 has a large potential for the following company targets“
Let’s build a use-case for the
top 3 i4.0 company targets as
plug-and-play
Might be perceived as a
follower (?)
Let’s dive into a Plug-and-Play use-case of an FMCG
manufacturer based on start-up solutions*
11 Elements of the Future Factory Stack
21
Prototyping/
Finding Supply
Rapid prototyping with the help of 3D
printing, platforms to find suitable suppliers
and vertically integrated factories.
Engineering
Tools
3D modeling, prototyping tools and
simulation platforms for designing products.
IoT/
Middleware
Get data from machines, connect offline
devices with online services.
Shopfloor
Guidance/
Apps
Enhance work instructions for complex
processes, process security and to ensure
the quality of production.
Wearables
Touch interfaces are ubiquitous in B2C and
people are used to personal devices. This
trend is recognizable in the industrial world.
Robotics
Software to program robotic behavior or
autonomous (co-) robots.
Energy
Monitoring/
Analytics
Monitor, analyze and optimize energy
consumption.
Analytics/
Efficiency
For a 360° overview and full control of the
whole production process. Measure and
analyze human workers and machine work.
Inspection
Companies that help in discovering issues on
the assembly line, e.g. with the help of
computer vision.
Predictive
Maintenance
Solutions for condition monitoring,
optimizing performance and reducing
downtime.
Asset Tracking/
Location
Analytics
Get transparency across the supply chain
with the help of tracking devices and
predictive / prescriptive analysis.
* as anything else takes long to implement
Fußnote
Überschrift
22
Aufsteller Füller Verdeckler Etikettierer Verpacker
FMCG Example Factory | Bottling
* Disclaimer: Prof. Focke is founder and managing director at the parent company of oee.cloud
Just assume we produce a product called „4711“ and
now let’s bring three i4.0 start-ups on stage
Filling Line „4711“
23
Positioner Filler Caper Labeler Packager
oee.cloud* Workerbase SFM Systems
- non-invasive sensors for
capturing OEE-data
- on-line AI analytics
- Industrial smart watch
wearables
- Shopfloor workflows
- Digital shopfloor management
All solutions are available for a monthly rental fee; no modification to the existing equipment,
no need to install a piece of software on premise
For more details: Focke,/Steinbeck, Steigerung der Anlagenproduktivität durch OEE-Management, Springer, Wiesbaden 2018
OEE is a globally standardized key performance indi-
cator for asset productivity; hard to measure until now
Overall Equipment Effectiveness
24
Availability Performance Qualityx x = OEE
- Breakdown
- Organizational stop
- Reduced speed
- Short stops
- Scrap
- Rework
KPI
OEE-concept is applicable to all manufacturing equipment
Collection of data for OEE-calculation and loss reasons is difficult: Tally sheet, manual, off-line
25
Non-intrusive | No modification of equipment
oee.cloud collects, analyzes and shares productivity
data with minimal equipment intrusion
Technology
26
Machine
Autonomous
collection of
machine status;
alternatively: direct
PLC-connection
Data Collection Data Usage
European

data center
Web Browser for
manual analysis
Messaging to shopfloor
employees
Digital shopfloor boards
Standard industry sensor,
e.g. light barrier
Tablet for
collecting
downtime
reasons
Andon
boards
oee.cloud collects the OEE data in real time; timeline
visualizes the production flow
Industrial Application
27
Data captured
by sensor
Fußnote
Überschrift
28
Downtime reason collection | Only cheap hardware
oee.cloud collects precise loss reasons for availability,
performance and quality in real time
Industrial Application
29
Loss reasons
collected via
tablet
Flexible loss reason collection with one click; several
stacked levels possible
Industrial Application
30
A small additional box with wifi-connection transforms
every display into an Andon-board
Andon
31
• Andon board on
every monitor,
display, TV, …
• Requirement:
• wifi-access
• HDMI-port
• No additional costs
Example video in German on YouTube: https://youtu.be/9LZr4BfpG_E
Alexa answers OEE questions, e.g. during shopfloor
management meetings or manager’s commute
Alexa Voice Interface
32
Alexa, how was the productivity of line 12
yesterday during the night shift?
The OEE of the night shift was 64 %.
How much did we produce yesterday on
line 12?
The total production of line 12 yesterday
was 45.870 pieces
Research project: Real-time detection of system
anomalies
Machine Learning
33
The AI-algorithm detects an
anomaly…
… assigns a reason…
… and evaluates the
consequence for the OEE
With a wearable like an industrial smart watch the
individual delivery of messages is secured
Workerbase Industrial Smart Watches
34
Reference

customer
- oee.cloud sends real-time notification to the smart
watch of a dedicated operations team member, e.g.
for
- machine setup time too long
- production speed slower than expected
- in case the loss reason is e.g. „missing material“
- Worker accepts or forwards the task: workflow
- Tasks can escalate in the organizational hierarchy
- Watch can also scan, take images, etc.
- Batterie lasts more than one shift
As the data is easily available via APIs, digital shopfloor
management is possible
Digital Shopfloor Management
35
5 Elements of the future factory stack have been
applied; i4.0 solutions in days not years
11 Elements of the Future Factory Stack
36
Prototyping/
Finding Supply
Rapid prototyping with the help of 3D
printing, platforms to find suitable suppliers
and vertically integrated factories.
Engineering
Tools
3D modeling, prototyping tools and
simulation platforms for designing products.
IoT/
Middleware
Get data from machines, connect offline
devices with online services.
Shopfloor
Guidance /
Apps
Enhance work instructions for complex
processes, process security and to ensure
the quality of production.
Wearables
Get data from machines, connect offline
devices with online services.
Robotics
Software to program robotic behavior or
autonomous (co-) robots.
Energy
Monitoring /
Analytics
Monitor, analyze and optimize energy
consumption.
Analytics /
Efficiency
For a 360° overview and full control of the
whole production process. Measure and
analyze human workers and machine work.
Inspection
Companies that help in discovering issues on
the assembly line, e.g. with the help of
computer vision.
Predictive
Maintenance
Solutions for condition monitoring,
optimizing performance and reducing
downtime.
Asset Tracking/
Location
Analytics
Get transparency across the supply chain
with the help of tracking devices and
predictive / prescriptive analysis.
All 3 start-ups are happy to answer your questions;
get in touch
Contact List
37
Prof. Dr. Markus Focke
Kalverbenden 31
52066 Aachen
markus@oee.cloud
+49 171/3620554
Thorsten Krüger
Aventinstraße 7
80469 München
thorsten@workerbase.com
+49 162/2685701
Dr. Christian Hertle
Otto-Berndt-Str. 2
64287 Darmstadt
hertle@sfmsystems.de
+49 6151/1620121
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
Let’s summarize 4 key take aways from this session
38
Summary
Quick implementation; you won’t have the chance to „standardize“ anyway; 

don’t wait for SAP if you want to generate competitive advantages
Use technology for continuous improvement; that’s generating financial benefits for your
company
It’s all about people! Focus on change management and sustainability: „Reach the human behind
the employee“
Data centric manufacturing is the key; a „data mindest“ needs to be build
In case of questions, just ask!
Prof. Dr.
Markus Focke
Professor für BWL, insbesondere 

Beschaffungs-, Produktions- und Logistikmanagement
FH Aachen
FB Wirtschaftswissenschaften
2. OG, Raum 279

T +49. 241. 6009 51922

F +49. 241. 6009 52280

E focke@fh-aachen.de
©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE
EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE
39

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The Future IOT Factory Stack

  • 1. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE State of the Art IOT: 
 The Future IOT Factory Stack Smart Factory@Consumer Goods day 2018 Prof. Dr. Markus Focke Leipzig, November 27, 2018 Porsche Consulting
  • 4. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Hype Cycle shows a lot of science fiction – but IOT Platforms and enabling technologies are present 4 PaaS = platform as a service; UAVs = unmanned aerial vehicles Source: Garter (2017), 3 Megatrends That Will Drive Digital Business, https://www.gartner.com/newsroom/id/3784363, accessed 17.11.17 IOT Gartner Hype Cycle for Emerging Technologies (2017)
  • 6. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Connected, transparency, assistance and decentrali- zation are major elements of a smart factory 6 connected integration Machines, devices, sensors and employees are connected and communicate with each other via internet-of-things oder world-wide-web. Main task is consolidating of functional separated information decentralised decisions Employees or machines make autonomous decisions and measures are performed at the lowest hierarchy layer as possible. This principle delegates responsibility back to the operational employees Systems assist employees with provision of aggregated, visual and understandable information. The target is to analyze the data and find the right measures as suggestions technical assistance transparent information Sensor devices enrich information systems of digital factories to create a virtual twin of the real world in real time. Generation of data and providing information to create real time transparency Principles of the Smart Factory Source: M. Hermann, T. Pentek, B. Otto: Design Principles for Industrie 4.0 Scenarios
  • 7. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Technological innovation pushes companies from „musicians to DJs of production technology“ 7 Trends for 2022 - Ubiquity: Omnipresent computer power and ubiquitous internet access are moving services via cloud into the internet - Big Data: Competence and willingness to measure all customer actions, provide new analytical insights and business models - Artificial Intelligence: Learning algorithms change our ways to find correlations and causations - Augmented reality and virtual reality lead to completely new forms of displaying infos - Raspberry Pi: Really small computer devices and standard operating systems - Python & Co: Establishment of simple to use standard programming languages Moving the barrier for innovation (from mastering technological issues) to understanding processes and the need for improvement Short-term improvements for individual problems possible 
 (RIP one-size-fits-all Solutions) Using sensors and analytical power-tools with almost no invest 
 (as-a-service models)
  • 8. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Software and analytics will rule manufacturing in the future; does your organization have the skills? 8 If you went to bed last night as an industrial company, you’re going to wake up today as a software and analytics company. – Jeffrey Immelt, former CEO of General Electric
  • 9. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Obstacles are high investments, low qualification of own personnel and ambiguity of potential gains 9 Obstacles for Implementation of Smart Factory * „Bitte geben Sie (..) an, ob Sie darin ein großes Hindernis für die Einführung von Industrie 4.0-Anwendungen sehen.“
 Source: EY, Industrie 4.0 Status Quo und Perspektiven, S. 28 low qualification of personell investment 
 too high security issues missing standards business model 
 not clear missing 
 IT Knowledge ambiguous 
 potential gain Total 100-499 employees 500+ employees
  • 10. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Implementation in small steps (instead of big projects) creates agile solution elements of a smart factory 10 Approach of Stepwise Implementation Source: Porsche Consulting Introduction of smart factory in small steps (call it CIP, agile, SCRUM… whatever) • Focus on specific problem and its solution • Individual solutions ensure fitting solutions • As-a-Service Models with no investment • AAS-platforms communi- cate via standard APIs • Cooperation with specialists (= start-ups) • Implementing solutions that solve real problems – no management toys • Evaluation of benefits directly possible • Low-risk strategy enables to take chances
  • 11. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE • Using hard- and software that is available today • Realisation is realistic and fits with available means and time • Proven fit with manufacturing needs and business potential is visible Right solutions: target corridor Job #1: Chose the right space of your solution 11 Solution Space pen & paper 0% 100% height of 
 innovation starship enterprise smart factory show-case
  • 12. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Culture, strategy and technology have to be in line for a successful application of smart factory ideas 12 Success Strategy Technology Culture no realization
 possible missing 
 acceptance direction 
 not clear • Open for changes • Collaboration • Executive Buy-In • Strong digital 
 skills • Cross-funktional teams, no silos • Risk taking attitude • Continuous improvement • Clearly defined vision • Digital strategy available • Existence of decision- making framework • Possibility of big steps vs. small improvements • Digital vision • Cloud • API-based • Automation • Continuous integration and implementation • Service oriented architecture • Analytics • Social • Mobile Culture + Strategy + Technology = Success Source: McKinsey Global Institute
  • 13. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Consumer devices offer high frequent innovations at low costs (much better than industry specific devices) 13 Consumer Devices Source: Apple, Statista 10 Iterations in 10 years • Average life cycle of consumer devices: 12 months • Extreme economies of scales (with world wide sales) lead to competitive prices (phones, tablets, 5G Devices) • Most specialized solutions lead 
 to x-times higher prices Adaptation of your 
 requirements necessary!
  • 15. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE It’s not you! Beyond others, start-ups are driving innovation in the upcoming era „Rather than coming from multinational firms as in the past, innovation now stems largely from research laboratories, digital platforms and startups. These are the players creating algorithms and developing use cases, they are the brains behind innovations in image recognition, natural language processing and automated driving.“ 15 Innovations come from Start-Ups It’s not longer possible to buy innovation from multinationals.
 You need to work with start-ups and think-tanks to achieve competitive advantage! Source: roland_berger_ai_strategy_for_european_startups.pdf (2018)
  • 16. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Work together with start-ups if you want to achieve competitive advantage – but you need to change! 16 * MVP = Minimum Viable Product Proof of technical feasibility Start-Up Working Scheme funding • Development of a working product (MVP*) • Hypotheses about customer requirements • Hypothesen über Kundenanforderungen • Find customer #1 funding Working with start-ups requires a positive drive to solve the problem - the classical customer- supplier-relationship will not work; it also needs knowledge and engagement also at „customer“ side • high pressure to get product working • happy to get customer feedback/ requirements • low on cash, but not profit oriented Validation of 
 business case • Re-Engineering MVP (complete overdo) • Learn real customer requirements • Pivoting • Grow customer base (references) • high pressure to get customers / references • happy to get customer feedback/ requirements • need to show real turnover Organizational 
 growth • Mature product • Stable organization (service level, processes) • Product with „fixed“ functionality • high pressure to get sales • not possible to fulfill every customers wish • standards oriented
  • 17. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE PointNine, a venture capital investor from Berlin, is observing the i4.0 start-up landscape; 250 entries 17 i4.0 Start-Up Landscape Source: https://medium.com/point-nine-news/industry-4-0-reinventing-the-factory-stack-bc9054398efa
  • 18. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE The (currently innovative) future factory stack consists of 11 elements 18 11 Elements of the Future Factory Stack Source: Robin Dechant, PointNine Capital, Berlin Prototyping/ Finding Supply Rapid prototyping with the help of 3D printing, platforms to find suitable suppliers and vertically integrated factories. Engineering Tools 3D modeling, prototyping tools and simulation platforms for designing products. IoT / Middleware Get data from machines, connect offline devices with online services. Shopfloor Guidance/ Apps Enhance work instructions for complex processes, process security and to ensure the quality of production. Wearables Touch interfaces are ubiquitous in B2C and people are used to personal devices. This trend is recognizable in the industrial world. Robotics Software to program robotic behavior or autonomous (co-) robots. Energy Monitoring/ Analytics Monitor, analyze and optimize energy consumption. Analytics/ Efficiency For a 360° overview and full control of the whole production process. Measure and analyze human workers and machine work. Inspection Companies that help in discovering issues on the assembly line, e.g. with the help of computer vision. Predictive Maintenance Solutions for condition monitoring, optimizing performance and reducing downtime. Asset Tracking/ Location Analytics Get transparency across the supply chain with the help of tracking devices and predictive / prescriptive analysis.
  • 19. State of the Art IOT:
 The Future IOT Factory Stack Smart Factory@Consumer Goods day 2018 Prof. Dr. Markus Focke Leipzig, November 25, 2018
  • 20. Source: EY, Industrie 4.0, Status Quo und Perspektiven (2017), S. 40; n = 557 industrial companies Flexibility, response time and equipment productivity are i4.0 goals - cost reduction not so much Motivation for Industry 4.0 20 Increase of production flexibility Faster response times Increase of OEE Optimization of customer support Development of innovative products Cost reduction 0 % 20 % 40 % 60 % 80 % 23 % 25 % 33 % 47 % 52 % 72 % Question: „Please rate if Industry 4.0 has a large potential for the following company targets“ Let’s build a use-case for the top 3 i4.0 company targets as plug-and-play Might be perceived as a follower (?)
  • 21. Let’s dive into a Plug-and-Play use-case of an FMCG manufacturer based on start-up solutions* 11 Elements of the Future Factory Stack 21 Prototyping/ Finding Supply Rapid prototyping with the help of 3D printing, platforms to find suitable suppliers and vertically integrated factories. Engineering Tools 3D modeling, prototyping tools and simulation platforms for designing products. IoT/ Middleware Get data from machines, connect offline devices with online services. Shopfloor Guidance/ Apps Enhance work instructions for complex processes, process security and to ensure the quality of production. Wearables Touch interfaces are ubiquitous in B2C and people are used to personal devices. This trend is recognizable in the industrial world. Robotics Software to program robotic behavior or autonomous (co-) robots. Energy Monitoring/ Analytics Monitor, analyze and optimize energy consumption. Analytics/ Efficiency For a 360° overview and full control of the whole production process. Measure and analyze human workers and machine work. Inspection Companies that help in discovering issues on the assembly line, e.g. with the help of computer vision. Predictive Maintenance Solutions for condition monitoring, optimizing performance and reducing downtime. Asset Tracking/ Location Analytics Get transparency across the supply chain with the help of tracking devices and predictive / prescriptive analysis. * as anything else takes long to implement
  • 22. Fußnote Überschrift 22 Aufsteller Füller Verdeckler Etikettierer Verpacker FMCG Example Factory | Bottling
  • 23. * Disclaimer: Prof. Focke is founder and managing director at the parent company of oee.cloud Just assume we produce a product called „4711“ and now let’s bring three i4.0 start-ups on stage Filling Line „4711“ 23 Positioner Filler Caper Labeler Packager oee.cloud* Workerbase SFM Systems - non-invasive sensors for capturing OEE-data - on-line AI analytics - Industrial smart watch wearables - Shopfloor workflows - Digital shopfloor management All solutions are available for a monthly rental fee; no modification to the existing equipment, no need to install a piece of software on premise
  • 24. For more details: Focke,/Steinbeck, Steigerung der Anlagenproduktivität durch OEE-Management, Springer, Wiesbaden 2018 OEE is a globally standardized key performance indi- cator for asset productivity; hard to measure until now Overall Equipment Effectiveness 24 Availability Performance Qualityx x = OEE - Breakdown - Organizational stop - Reduced speed - Short stops - Scrap - Rework KPI OEE-concept is applicable to all manufacturing equipment Collection of data for OEE-calculation and loss reasons is difficult: Tally sheet, manual, off-line
  • 25. 25 Non-intrusive | No modification of equipment
  • 26. oee.cloud collects, analyzes and shares productivity data with minimal equipment intrusion Technology 26 Machine Autonomous collection of machine status; alternatively: direct PLC-connection Data Collection Data Usage European
 data center Web Browser for manual analysis Messaging to shopfloor employees Digital shopfloor boards Standard industry sensor, e.g. light barrier Tablet for collecting downtime reasons Andon boards
  • 27. oee.cloud collects the OEE data in real time; timeline visualizes the production flow Industrial Application 27 Data captured by sensor
  • 29. oee.cloud collects precise loss reasons for availability, performance and quality in real time Industrial Application 29 Loss reasons collected via tablet
  • 30. Flexible loss reason collection with one click; several stacked levels possible Industrial Application 30
  • 31. A small additional box with wifi-connection transforms every display into an Andon-board Andon 31 • Andon board on every monitor, display, TV, … • Requirement: • wifi-access • HDMI-port • No additional costs
  • 32. Example video in German on YouTube: https://youtu.be/9LZr4BfpG_E Alexa answers OEE questions, e.g. during shopfloor management meetings or manager’s commute Alexa Voice Interface 32 Alexa, how was the productivity of line 12 yesterday during the night shift? The OEE of the night shift was 64 %. How much did we produce yesterday on line 12? The total production of line 12 yesterday was 45.870 pieces
  • 33. Research project: Real-time detection of system anomalies Machine Learning 33 The AI-algorithm detects an anomaly… … assigns a reason… … and evaluates the consequence for the OEE
  • 34. With a wearable like an industrial smart watch the individual delivery of messages is secured Workerbase Industrial Smart Watches 34 Reference
 customer - oee.cloud sends real-time notification to the smart watch of a dedicated operations team member, e.g. for - machine setup time too long - production speed slower than expected - in case the loss reason is e.g. „missing material“ - Worker accepts or forwards the task: workflow - Tasks can escalate in the organizational hierarchy - Watch can also scan, take images, etc. - Batterie lasts more than one shift
  • 35. As the data is easily available via APIs, digital shopfloor management is possible Digital Shopfloor Management 35
  • 36. 5 Elements of the future factory stack have been applied; i4.0 solutions in days not years 11 Elements of the Future Factory Stack 36 Prototyping/ Finding Supply Rapid prototyping with the help of 3D printing, platforms to find suitable suppliers and vertically integrated factories. Engineering Tools 3D modeling, prototyping tools and simulation platforms for designing products. IoT/ Middleware Get data from machines, connect offline devices with online services. Shopfloor Guidance / Apps Enhance work instructions for complex processes, process security and to ensure the quality of production. Wearables Get data from machines, connect offline devices with online services. Robotics Software to program robotic behavior or autonomous (co-) robots. Energy Monitoring / Analytics Monitor, analyze and optimize energy consumption. Analytics / Efficiency For a 360° overview and full control of the whole production process. Measure and analyze human workers and machine work. Inspection Companies that help in discovering issues on the assembly line, e.g. with the help of computer vision. Predictive Maintenance Solutions for condition monitoring, optimizing performance and reducing downtime. Asset Tracking/ Location Analytics Get transparency across the supply chain with the help of tracking devices and predictive / prescriptive analysis.
  • 37. All 3 start-ups are happy to answer your questions; get in touch Contact List 37 Prof. Dr. Markus Focke Kalverbenden 31 52066 Aachen markus@oee.cloud +49 171/3620554 Thorsten Krüger Aventinstraße 7 80469 München thorsten@workerbase.com +49 162/2685701 Dr. Christian Hertle Otto-Berndt-Str. 2 64287 Darmstadt hertle@sfmsystems.de +49 6151/1620121
  • 38. ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE Let’s summarize 4 key take aways from this session 38 Summary Quick implementation; you won’t have the chance to „standardize“ anyway; 
 don’t wait for SAP if you want to generate competitive advantages Use technology for continuous improvement; that’s generating financial benefits for your company It’s all about people! Focus on change management and sustainability: „Reach the human behind the employee“ Data centric manufacturing is the key; a „data mindest“ needs to be build
  • 39. In case of questions, just ask! Prof. Dr. Markus Focke Professor für BWL, insbesondere 
 Beschaffungs-, Produktions- und Logistikmanagement FH Aachen FB Wirtschaftswissenschaften 2. OG, Raum 279
 T +49. 241. 6009 51922
 F +49. 241. 6009 52280
 E focke@fh-aachen.de ©FHAACHENUNIVERSITYOFAPPLIEDSCIENCES|PROF.DR.MARKUSFOCKE EUPENERSTR.70|52066AACHEN|WWW.FH-AACHEN.DE 39